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BenchLM
Anthropic logo
Model A
Claude Haiku 4.5

Anthropic

52.91/100

Supported · Public rank #110

90% interval 40.5–65.4

Claude Haiku 4.5 vs MiniMax M2.7

Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.14/100

Supported · Public rank #95

90% interval 43.8–66.5

Decision reading

MiniMax M2.7 has the higher public score estimate, 55.14 versus 52.91, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    MiniMax M2.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Haiku 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
5
Claude Haiku 4.5 only
5
MiniMax M2.7 only
18
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Claude Haiku 4.5
27.0
Supported · #143/153
MiniMax M2.7
41.1
Estimated · #110/153
Basis
BenchAlign lane · 2 vs 7 public rows
Reading
Directional only

Coding

Directional only
Claude Haiku 4.5
27.4
Supported · #144/152
MiniMax M2.7
48.6
Estimated · #68/152
Basis
BenchAlign lane · 4 vs 11 public rows
Reading
Directional only

Knowledge

Directional only
Claude Haiku 4.5
44.3
Estimated · #115/183
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 2 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Haiku 4.5
Not ranked
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Haiku 4.5
28.9
Unranked · 2 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Haiku 4.5
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Haiku 4.5
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Haiku 4.5
Not ranked
MiniMax M2.7
93.0
#10/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Haiku 4.5
$0.0035
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Haiku 4.5
$0.065
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Haiku 4.5
$0.09
Does not fit in one request
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

Claude Haiku 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Claude Haiku 4.5

200K

Claude API pricing

MiniMax M2.7

200K

API model ID

Claude Haiku 4.5

claude-haiku-4-5-20251001

Claude API pricing

MiniMax M2.7

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Haiku 4.5

$0.1 per 1M cached input tokens

Claude API pricing

MiniMax M2.7

Not published

Documented inputs

Claude Haiku 4.5

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Claude Haiku 4.5

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Claude Haiku 4.5

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Claude Haiku 4.5

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Claude Haiku 4.5

Proprietary

MiniMax M2.7

Open Weight

License

Claude Haiku 4.5

Proprietary

MiniMax M2.7

Open Weight

Release date

Claude Haiku 4.5

2025-10-15

MiniMax M2.7

2026-03-18

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
MiniMax M2.7 has the higher public score estimate, 55.14 versus 52.91, but the 90% score intervals overlap.
Workload cost
Repository review: $0.065 vs $0.0186. Cache-heavy agent loop: $0.09 vs $0.078.
Context tradeoff
Both models list 200K.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence28 rows

Agentic

  • JobBench

    Claude Haiku 4.516.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Haiku 4.543.8%
    Source
    MiniMax M2.748.7%
    Source

    MiniMax M2.7 leads this result

  • Terminal-Bench 2.0

    Claude Haiku 4.5—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Claude Haiku 4.5—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Claude Haiku 4.5—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Claude Haiku 4.5—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Haiku 4.5—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Claude Haiku 4.5—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Haiku 4.573.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • VulcanBench v3

    Claude Haiku 4.576.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Haiku 4.541.2%
    Source
    MiniMax M2.779.9%
    Source

    MiniMax M2.7 leads this result

  • SWE-bench (Vals)

    Claude Haiku 4.566.6%
    Source
    MiniMax M2.773.8%
    Source

    MiniMax M2.7 leads this result

  • SWE-bench Verified*

    Claude Haiku 4.5—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Claude Haiku 4.5—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Haiku 4.5—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Haiku 4.5—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Claude Haiku 4.5—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Claude Haiku 4.5—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Claude Haiku 4.5—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Haiku 4.5—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Claude Haiku 4.5—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Haiku 4.572.2%
    Source
    MiniMax M2.786.6%
    Source

    MiniMax M2.7 leads this result

  • MMLU-Pro (Vals)

    Claude Haiku 4.578.7%
    Source
    MiniMax M2.780.4%
    Source

    MiniMax M2.7 leads this result

  • GPQA-D

    Claude Haiku 4.5—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Haiku 4.5—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Haiku 4.55.903%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Haiku 4.52.083%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    Claude Haiku 4.5—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Haiku 4.5 or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 55.14 versus 52.91, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Haiku 4.5 or MiniMax M2.7?

MiniMax M2.7 scores higher for coding on the public lane, 48.6 to 27.4. MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Haiku 4.5 or MiniMax M2.7?

MiniMax M2.7 scores higher for agentic tasks on the public lane, 41.1 to 27. MiniMax M2.7 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Haiku 4.5 or MiniMax M2.7?

For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.0009 on MiniMax M2.7; repository review costs $0.065 and $0.0186; the cache-heavy agent loop costs $0.09 and $0.078. Claude Haiku 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Haiku 4.5 or MiniMax M2.7?

Both models list the same context window, 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

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